Methods for monitoring distributed energy storage safety and internet of things systems thereof

ABSTRACT

The present disclosure discloses a method for monitoring distributed energy storage safety and an Internet of Things system. The method includes utilizing a data acquiring unit to monitor operating data on a liquefied natural gas (LNG) storage device and transmitting the operating data to an LNG distributed energy management platform; the management platform determining abnormal data in the perception information according to an anomaly judgment condition and performing a pseudo data verification and labeling on the abnormal perception information; performing an anomaly prediction analysis according to an early warning mechanism; sending an alarm prompt to field maintenance personnel according to the abnormal data; and the field maintenance personnel performing a two-way confirmation with the management platform after processing. The present disclosure can determine abnormal data and verify and screen out pseudo data, send an alarm prompt to maintenance personnel, maintain an abnormal storage device, henceforth improving the efficiency of safety supervision of energy storage.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority of Chinese Patent Application No.202210563072.1, filed on May 23, 2022, and Chinese Patent ApplicationNo. 202310425941.9, filed on Apr. 20, 2023, the entire contents of whichare incorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to the technical field of monitoringenergy storage safety, and in particular, to a method for monitoringdistributed energy storage safety and an Internet of Things system.

BACKGROUND

In August 2021, the Department of Petroleum and Natural Gas of theNational Energy Administration and other departments released the “ChinaNatural Gas Development Report (2021)”. The report shows that China'smultiple supply system of natural gas continues to improve, and the “onenetwork across the country” has basically taken shape. A total of 46,000kilometers of long-distance pipelines have been built, and the totalmileage of natural gas pipelines across China has reached about 110,000kilometers. However, there are still a large number of economicallyunderdeveloped areas such as remote suburbs, counties, mountainousareas, rural areas, and areas with insufficient pipelines, where it isimpossible to use safe and clean natural gas with obvious advantages inlife and work. According to statistics, nearly 600 million people inChina still cannot use natural gas.

However, gas markets in outer suburbs, counties, mountainous areas, andrural areas are potential markets for urban gas. The energy supply inthese areas is an integral part of a country's entire energy system, andits supply and consumption will inevitably affect China's energy supplyand demand. At present, the focus of urban construction is graduallyshifting from the urban area to the outer suburbs, counties, mountainousareas, and rural areas, so it is necessary to establish an efficient,safe, and economical energy supply system.

Liquefied Natural Gas (LNG) is now attracting attention as a cleanenergy source. The combustion of natural gas produces only 50% and 20%of the carbon dioxide and nitrogen oxides produced by combustion ofcoal, which is ¼ the pollution of LPG and 1/800 the pollution of coal.Due to the high investment cost of pipeline laying, LNG gasificationstation has better economic efficiency than pipeline gas. In small andmedium-sized towns, LNG gasification stations can be used as a gassource for residents. In addition, it can also be used for commercial,business, and household heating, etc. By building a virtual pipelinenetwork system, gas can be sent to the countryside to solve the currentsituation that nearly 600 million people in China still cannot usenatural gas.

Existing LNG storage devices cannot realize an automatic and intelligentperception of storage device status, and data transmission haslimitations and certain security risks. The security of the datatransmission in the backend is low, and when analyzing the abnormal dataof the storage device status data, the pseudo data in the abnormal datacannot be effectively removed, and the abnormal status of the storagedevice of the gasification station cannot be warned in advance. Inaddition, LNG evaporates and generates gas in the storage device, whichincreases the pressure in the LNG storage device, so it is necessary toadjust the pressure in time.

Therefore, it is hoped to provide a method for monitoring distributedenergy storage safety and an Internet of Things system, which canefficiently and intelligently monitor the status of LNG storage devices,transmit, and analyze data safely and accurately, remove pseudo data inabnormal data, and perform reliable and accurate prediction of futurepressure change data, so as to facilitate timely detection ofabnormalities and processing.

SUMMARY

The purpose of the present disclosure is to overcome deficiencies of theprior art and provide a method for monitoring distributed energy storagesafety and a system, which acquires device monitoring data of LNGstorage devices through multiple sensors and uploads the devicemonitoring data to an LNG distributed energy management platform foranalysis in real time. The LNG distributed energy management platformestablishes a parameter change trend prediction model to analyze themonitoring data in real time, determine abnormal data and perform apseudo data verification on the abnormal data to screen out pseudo data,and finally send an alarm prompt to maintenance personnel through anearly warning mechanism for maintenance on an abnormal storage device,so as to improve the efficiency of safety monitoring of energy storage.

The purpose of the present disclosure is achieved through the followingtechnical solutions.

A method for monitoring distributed energy storage safety, comprisingfollow steps.

Step 1: monitoring, by utilizing a data acquiring unit, an LNG storagedevice, and transmitting perception information after symmetricencryption to an LNG distributed energy management platform through anLNG distributed energy storage sensor network platform.

Step 2: decrypting, by the LNG distributed energy management platform,the encrypted perception information, performing an anomaly judgment ondecrypted perception information according to a preset anomaly judgmentcondition, and performing a pseudo data verification on and labeling theabnormal perception information; performing an anomaly predictionanalysis on operating data of the LNG storage device according to anearly warning mechanism and an alarm.

Step 3: sending, by an LNG distributed energy storage maintenancepersonnel sensor network platform, an alarm prompt to field maintenancepersonnel for an inspection and processing according to a tank number ofa storage device corresponding to the abnormal data obtained by theanomaly prediction analysis and anomaly judgment.

Step 4: performing, by the field maintenance personnel, a two-waymaintenance confirmation with the LNG distributed energy managementplatform through the LNG distributed energy storage maintenancepersonnel sensor network after completing the inspection and processingto complete maintenance of the abnormal storage device.

Step 1 specifically includes that: monitoring, by utilizing the dataacquiring unit, the LNG storage device, perceiving and acquiringpressure, temperature, and position data on the LNG storage device,obtaining encrypted perception information through performing ananalog-to-digital conversion on perception information by the dataacquiring unit and symmetrically encrypting the perception informationby adopting a microsoft point-to-point encryption (MPPE) and InternetProtocol Security (IPSec) mechanism in a binary mode; and sending, bythe data acquiring unit, authentication information to an LNGdistributed energy management platform at a designated address throughan LNG distributed energy storage sensor network platform, after passinga two-way symmetric authentication, establishing a unique communicationchannel between the data acquiring unit and the LNG distributed energymanagement platform to transmit the encrypted perception information.

Step 2 specifically includes that: decrypting, by the LNG distributedenergy management platform, the encrypted perception information,performing the anomaly judgment on the decrypted perception informationaccording to the preset anomaly judgment condition, and screening outabnormal perception information; performing the pseudo data verificationon the abnormal perception information by utilizing the pseudo dataverification manner, identifying and labeling a type of pseudo datacaused by an external environmental interference; performing the anomalyprediction analysis on the operating data of the LNG storage deviceaccording to the early warning mechanism.

Performing the pseudo data verification on the abnormal perceptioninformation by utilizing the pseudo data verification manner includes:establishing the pseudo data verification manner, setting an error codein a sensor program of the data acquiring unit to simulate a sensorvalue during a real electromagnetic interference for pseudo datagenerated by an electromagnetic interference in a field maintenanceprocess in advance, setting an anomaly analysis result of the LNGdistributed energy management platform as pseudo data of theelectromagnetic interference; for pseudo data generated by atransmission or device failure, randomly creating a sensor ortransmission line failure, and setting the anomaly analysis resultlabeled by the LNG distributed energy management platform as pseudo dataof the sensor or transmission line; and performing a pseudo dataanalysis on the screened abnormal perception information, and labeling acorresponding type of pseudo data by utilizing the pseudo dataverification manner.

The performing the anomaly prediction analysis on the operating data ofthe LNG storage device according to the early warning mechanism includesfollow processes.

Data preprocessing: adopting a Holt double-parameter linear exponentialsmoothing method to smooth the decrypted perception information toobtain a monitoring time series x_(t).

Model initialization: an initialization model order p=1, a forwardpredicted step size np=np₀; p denotes a machine order of the parameterchange trend prediction model, np denotes a count of forward steps forthe parameter change trend prediction model to perform the anomalyprediction analysis, and np₀ denotes an initialization value of theanomaly prediction analysis.

Model establishment: establishing an initial auto-regression movingaverage (ARMA) model based on the monitoring time series x_(t).

Determining a length of a modeling sample: determining an integermultiple of an inverse of an interval between two adjacent frequenciesin a temporal frequency domain of the perception information as thelength of the modeling sample through time series analysis.

Estimating model parameter: estimating the model parameter by utilizinga least square method.

Inspecting the model and determining an order: determining a machineorder p of a parameter change trend predicting model to obtain a finalparameter trend predicting model ARMA (2p, 2p−1) by adopting an Akaikeinformation criterion (AIC).

Predicting parameter: obtaining a prediction interval by calculating acontinuous forward predicted step size np.

Analyzing the abnormal data: obtaining an operating prediction result ofthe LNG storage device through calculating a best prediction result anda corresponding prediction interval corresponding to the best predictionresult by adopting a dynamically correcting ARMA prediction method, anddetermining whether the operating prediction result is the abnormal dataaccording to the preset anomaly judgment condition.

The data preprocessing specifically includes follow processes.

Processing the abnormal perception data, forming the monitoring timeseries {x_(t), t=1, 2, . . . , N} for the perceived and acquiredoperating data of the LNG storage device, and for abnormal monitoringdata being zero or with a low probability sensor value, calculating aone-step smoothing value F_(t) of first N_(x) numbers by monitoring thefirst N_(x) numbers in the monitoring time series to replace theabnormal monitoring data, and selecting actual monitoring operating datato obtain a length N_(x) of the monitoring time series used for asmoothing calculation.

Processing missing data, for a missing sequence {x_(t), t=1, 2, . . . }formed by original monitoring data, firstly obtaining the length N_(x)of the monitoring time series of the original data required for thesmoothing calculation according to an actual monitoring operating dataanalysis; and setting a count of smoothing steps m, and for gasconcentration monitoring values {x_(t), t=1, 2, . . . , N_(x)} of thefirst N_(x) points of missing data points, continuously performing thesmoothing calculation of m steps to obtain a final smoothed valueF_(t+m), and finally inserting the final smoothed value F_(t+m) into themissing sequence to form a complete monitoring data time series.

The dynamically correcting ARMA prediction method specifically includesfollow processes.

Evaluating a predicted error, for previous j−1 predictions, calculatingan average value of prediction errors of previous n predictions, andobtaining an error minimum value and an error subminimum value.

Determining an effective model order, determining model orders p₁ and p₂when the predicted error minimum value and the error subminimum valueare obtained as effective model orders of the previous j−1 predictions.

Modeling with current data, for an analysis sequence formed by operatingmonitoring data of the current LNG storage device, obtaining an optimalorder p₀ through the ARMA model for parameter estimation and validityinspection.

Predicting model, taking p=p₀, p₁, p₂ as an order respectively toperform an operating data parameter prediction, and obtaining predictionresults X=[x_(j1), x_(j2), x_(j3)].

Calculating the best prediction result, calculating an average value ofeach element of X=[x_(j1), x_(j2), x_(j3)] to obtain a final predictionresult as the best prediction result.

Step 4 specifically includes: the field maintenance personnel sendingprocessing information to the management platform through the LNGdistributed energy storage maintenance personnel sensor network aftercompleting the inspection and processing, and the management platformconfirming whether the processing is completed; the LNG distributedenergy management platform obtaining processed tank perceptioninformation through the LNG distributed energy storage sensor networkplatform and confirming that the field maintenance personnel completesthe processing if a status of the processed tank perception informationis normal, and feeding back to the field maintenance personnel.

An Internet of Things system for monitoring distributed energy storagesafety, which is realized by using a method for monitoring distributedenergy storage safety, wherein the system includes an object platform, asensor network platform, a management platform, a service platform, anda user platform.

The LNG distributed energy storage object platform is configured tomonitor and perceive operating data of an LNG storage device, andtransmit the perception information after symmetric encryption to theLNG distributed energy management platform through a correspondingsensor network platform.

The sensor network platform is configured to realize a communicationconnection for perception and control between the LNG distributed energymanagement platform and the LNG distributed energy object platform.

The LNG distributed energy management platform is configured to performan anomaly judgment and anomaly prediction analysis based on acquiredoperating data, and send the alarm prompt to field maintenance personnelfor inspection and processing through the sensor network platformaccording to a tank number of a storage device corresponding to abnormaldata obtained by the anomaly judgment and anomaly prediction analysis.

The service platform is configured to obtain perception informationdemanded by a user from the LNG distributed energy management platformfor analysis and storage, and receive control information sent by theuser for processing and send processed control information to the LNGdistributed energy management platform.

The user platform is configured to obtain the operating data of the LNGstorage device from the service platform for various users and send thecontrol information to the service platform.

Beneficial effects of the present disclosure are as follows.

1. The present disclosure can accurately locate a tank numbercorresponding to abnormal data by automatically acquiring informationsuch as pressure, temperature, and location of LNG storage devices inreal-time for anomaly analysis, and improve maintenance efficiency ofthe field maintenance personnel by sending an alarm prompt to fieldmaintenance personnel of an LNG distributed energy object platform forinspection and processing through an LNG distributed energy storagemaintenance personnel sensor network platform.

2. The present disclosure performs a pseudo data verification ondetermined abnormal data through a pseudo data verification manner,which can intuitively and clearly identify pseudo data and prompt a typeof pseudo data displayed by a sensor within this time period, making itconvenient for the field maintenance personnel to carry out maintenanceand improve maintenance efficiency.

3. The present disclosure also performs an anomaly prediction analysison the operation trend of LNG storage device and obtain an operatingprediction result of the LNG storage device in a correspondingprediction interval by establishing a ARMA parameter trend predictingmodel, and determines whether the operating prediction result isabnormal data, so that the abnormal data can be detected and earlywarning can be sent in time, further improving the efficiency of safetymonitoring of an LNG storage device.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are not limited, in theseembodiments, the same numbers denote the same structures, wherein:

FIG. 1 is a flowchart illustrating an exemplary process of a method formonitoring distributed energy storage safety according to someembodiments of the present disclosure;

FIG. 2 is a flowchart illustrating an exemplary process of an anomalyprediction analysis manner according to some embodiments of the presentdisclosure;

FIG. 3 is a schematic diagram illustrating an Internet of Things systemfor monitoring distributed energy storage safety according to someembodiments of the present disclosure.

FIG. 4 is a flowchart illustrating an exemplary process for managing LNGstorage safety according to some embodiments of the present disclosure;

FIG. 5 is a flowchart illustrating an exemplary process for determiningat least one set of pressure change data at at least one future timepoint according to some embodiments of the present disclosure;

FIG. 6 is a schematic diagram illustrating an exemplary process fordetermining at least one future time point according to some embodimentsof the present disclosure.

DETAILED DESCRIPTION

In order to have a clearer understanding of the technical features,purposes, and beneficial effects of the present disclosure, thetechnical solutions of the present disclosure are now described indetail below. Apparently, the described embodiments are some embodimentsof the present disclosure, but not all embodiments, and should not beunderstood as limiting the applicable scope of the present disclosure.Based on the embodiments of the present disclosure, all otherembodiments obtained by persons of ordinary skill in the art withoutmaking creative efforts belong to the protection scope of the presentdisclosure.

Embodiment 1

In the embodiment, as shown in FIG. 1 , a method for monitoringdistributed energy storage safety includes the following steps.

Step 1: monitoring, by utilizing a data acquiring unit, an LNG storagedevice, and transmitting perception information after symmetricencryption to an LNG distributed energy management platform through anLNG distributed energy storage sensor network platform.

Step 2: decrypting, by the LNG distributed energy management platform,the encrypted perception information, performing an anomaly judgment ondecrypted perception information according to a preset anomaly judgmentcondition, and performing a pseudo data verification on and labeling theabnormal perception information utilizing a pseudo data verificationmanner, performing an anomaly prediction analysis on operating data ofthe LNG storage device according to an early warning mechanism andalarm.

Step 3: sending, by an LNG distributed energy storage maintenancepersonnel sensor network platform, an alarm prompt to field maintenancepersonnel for an inspection and processing according to a tank number ofa storage device corresponding to the abnormal data obtained by theanomaly prediction analysis and anomaly judgment.

Step 4: performing, by the field maintenance personnel, a two-wayconfirmation with the LNG distributed energy platform managementplatform through the LNG distributed energy storage maintenancepersonnel sensor network platform after completing the inspection andprocessing to complete maintenance of the abnormal storage device.

In the embodiment, the data acquiring unit is a comprehensiveapplication technology based on a modern and mature, and advancedelectronic measuring technology and Internet of Things technology. Thedata acquiring unit includes a GPS/Beidou locator, a pressure sensor, atemperature sensor, and a monitoring terminal. A working principle ofthe data acquiring unit is that the GPS/Beidou locator acquires alocation of the LNG storage device and pressure at the bottom of thetank, a temperature sensor perceives and acquires pressure and atemperature analog signal of the LNG storage device, and transmits themto a monitoring terminal at the top of the tank through a cable, and anembedded microprocessor of the monitoring terminal converts the analogsignal into digital information and transmits the data information and acorresponding tank number through the sensor network platform to the LNGdistributed energy management platform for further analysis andprocessing. Tasks of the embedded microprocessor are to convert theanalog signal into computer data suitable for Internet transmissionthrough a certain encoding method, and to minimize an error of acquireddata through a “precision memory interpolation algorithm” and a mostadvanced and accurate 16-bit algorithm.

In the embodiment, step 1 specifically includes that: monitoring, byutilizing a data acquiring unit, a liquefied natural gas (LNG) storagedevice, perceiving and acquiring pressure, temperature, and positiondata on the LNG storage device, obtaining encrypted perceptioninformation through performing an analog-to-digital conversion onperception information by the data acquiring unit and symmetricallyencrypting the perception information by adopting a microsoftpoint-to-point encryption (MPPE) and Internet Protocol Security (IPSec)mechanism in a binary mode; actively sending, by the data acquiringunit, authentication information to an LNG distributed energy managementplatform at a designated address through an LNG distributed energystorage sensor network platform, after passing a two-way symmetricauthentication, establishing a unique communication channel between thedata acquiring unit and the LNG distributed energy management platformto transmit the encrypted perception information.

The present disclosure adopts a microsoft point-to-point encryption(MPPE) and Internet Protocol Security (IPSec) mechanism to symmetricallyencrypt the perception information and manage keys by a public-privatekey verification. MPPE enables terminals to communicate securely fromanywhere in the world. MPPE encryption ensures secure transmission ofdata with a minimal public key cost. These means of authentication andencryption are enforced by a remote server.

In the embodiment, step 2 specifically includes that: decrypting, by theLNG distributed energy management platform, the encrypted perceptioninformation, performing an anomaly judgment on decrypted perceptioninformation according to a preset anomaly judgment condition, andscreening out abnormal perception information; performing a pseudo dataverification on the abnormal perception information utilizing a pseudodata verification manner, identifying and labeling a type of pseudo datacaused by an external environmental interference; performing an anomalyprediction analysis on operating data of the LNG storage deviceaccording to an early warning mechanism.

In the embodiment, the performing a pseudo data verification on theabnormal perception information by utilizing a pseudo data verificationmanner includes: establishing the pseudo data verification manner,setting an error code in a sensor program of the data acquiring unit tosimulate a sensor value during a real electromagnetic interference forpseudo data generated by an electromagnetic interference in a fieldmaintenance process in advance, setting an anomaly analysis result ofthe LNG distributed energy management platform as pseudo data of theelectromagnetic interference; for pseudo data generated by atransmission or device failure, randomly creating a sensor ortransmission line failure, and setting the anomaly analysis resultlabeled by the LNG distributed energy management platform as pseudo dataof the sensor or transmission line; and performing a pseudo dataanalysis on the screened abnormal perception information, and labeling acorresponding type of pseudo data by utilizing the pseudo dataverification manner.

In the embodiment, an auto-regression moving average (ARMA) model is animportant method to study a time series, which is composed based on anauto-regression (AR) model and a moving average (MA) model. In recentyears, the ARMA has also become one of the important methods in studyingtime series forecasting problems. For a smooth time series {x_(t)}, ifx_(t) is not only related to each value x_(t−1), x_(t−2), . . . x_(t−n)first n steps, but also to each disturbance a_(t−1), a_(t−2), a_(t−m) offirst m steps (n, m=1, 2, . . . ), then a general ARMA model may beobtained based on an idea of multiple linear regression as follows.

x_(t) = φ₁x_(t − 1) + φ₂x_(t − 2) + ⋯ + φ_(n)x_(t − n) − θ₁a_(t − 1) + θ₂a_(t − 2) + ⋯ + θ_(m)a_(t − m) + a_(t), a_(t) ∼ NID(0, σ_(a)²)

The equation represents an n-order auto-regressive and m-order movingaverage model, which is denoted as ARMA (n, m); φ_(i)(i=1, 2, . . . ,n), θ_(j)(j=1, 2, . . . , m) denote model parameters.

This embodiment adopts a generalized least squares method for modelparameter estimation. An idea of the generalized squares method for ARMA(n, m) model parameter estimation is to convert an ARMA model into theAR model, perform a series of linear least squares estimations, and thenreduce an obtained AR model to the ARMA model.

In the embodiment, as shown in FIG. 2 , the performing an anomalyprediction analysis on operating data of the LNG storage deviceaccording to an early warning mechanism includes the following steps.

Data preprocessing: adopting a Holt double-parameter linear exponentialsmoothing method to smooth the decrypted perception information toobtain a monitoring time series x_(t).

Model initialization: an initialization model order p=1, a forwardpredicted step size np=np₀; p denotes a machine order of the parameterchange trend prediction model, np denotes a count of forward steps forthe parameter change trend prediction model to perform the anomalyprediction analysis, and np₀ denotes an initialization value of theanomaly prediction analysis.

Model establishment: establishing an initial auto-regression movingaverage (ARMA) model based on the monitoring time series x_(t).

Determining a length of a modeling sample: determining an integermultiple of an inverse of an interval between two adjacent frequenciesin a temporal frequency domain of the perception information as thelength of the modeling sample through time series analysis.

Estimating model parameter: estimating the model parameter by utilizinga least square method.

Inspecting the model and determining an order: determining a machineorder p of a parameter change trend predicting model to obtain a finalparameter trend predicting model ARMA (2p, 2p−1) by adopting an Akaikeinformation criterion (AIC).

Predicting parameter: obtaining a prediction interval by calculating acontinuous forward predicted step size np.

Analyzing the abnormal data: obtaining an operating prediction result ofthe LNG storage device through calculating a best prediction result anda corresponding prediction interval corresponding to the best predictionresult by adopting a dynamically correcting ARMA prediction method, anddetermining whether the operating prediction result is the abnormal dataaccording to the preset anomaly judgment condition; the preset anomalyjudgment condition is that the operating prediction result of the LNGstorage device exceeds a set pressure and temperature.

The early warning mechanism is that when the predicted result of theARMA model is abnormal data, the alarm prompt may be sent to a handheldterminal of the field maintenance personnel respectively through thesensor network platform to remind the field maintenance personnel forprocessing.

In the embodiment, a data feature of the LNG storage device is analyzedfirst, and a analysis result is as follows.

(1) Data features. Real-time monitoring data of a monitoring andsurveillance system generally takes a time interval of 10 s to 30 s as acycle. Although actual monitoring data may be intermittent, it may besupplemented by interpolation and smoothing, etc., so data with an eventime interval may be obtained finally. Data items related to real-timemonitoring data include a monitoring point location, a sensor number,time, and a monitoring value. Manual inspection data is generally databased on cycles of 2 h, 8 h, or 24 h, and the manual inspection data mayalso be converted into data with a uniform cycle by the interpolation,for example, data in an inspection daily inspection report of a storagedevice may be interpolated to generate data for every 8 h. On the otherhand, the monitoring system records statistical values of the real-timemonitoring data of the device every 5 minutes and every hour, such as anaverage value, a maximum value, and a minimum value, so the real-timemonitoring data may be matched with the manual inspection data, and thedata obtained by the interpolating the manual detection data shows asmooth change trend of device detection parameters in a long period oftime, while a real-time monitoring data curve reflects an instant changeof each local device detection parameters in a long period of time.Therefore, these two types of data may be combined for an early warninganalysis.

(2) Statistical features. Statistical parameters of the real-timemonitoring data, such as an average value and maximum value within acertain period of time, may reflect statistical features of devicemonitoring parameters such as pressure and temperature within the periodof time; a manual inspection value is the same as a single monitoringvalue, which belongs to an instantaneous measurement value correspondingto a specific time and place. A time interval of a manual inspection isrelatively long, so a statistical analysis of the manual inspection isusually conducted for a longer time interval (such as one month).However, for the monitoring data of the device, statistics may be madewith a cycle of the manual inspection data as a time length, and theobtained statistical parameters reflect a change of pressure andtemperature during the period of the manual inspection to a certainextent. Therefore, a data interval formed by an average value and amaximum value of the monitoring data of the device may contain a manualinspection value.

(3) Correlation features. In the process of the early warning analysis,two aspects are considered. For a predictive early warning of pressureand temperature change trends, based on a correlation of theinspection/monitoring data, the real-time monitoring data is used as ananalysis object, and the manual inspection data is used to verify thevalidity of the model.

In the embodiment, based on the above feature analysis content, thepresent disclosure proposes a method for processing abnormal data andmissing data, which effectively guarantees to improve the reliability ofan abnormal data analysis result of the LNG storage device. The datapreprocessing specifically includes following processes.

Processing the abnormal perception data, forming the monitoring timeseries {x_(t), t=1, 2, . . . , N} for the perceived and acquiredoperating data of the LNG storage device, and for abnormal monitoringdata being zero or with a low probability sensor value, calculating aone-step smoothing value F_(t) of first N_(x), numbers by monitoring thefirst N_(x) numbers in the monitoring time series to replace theabnormal monitoring data, and selecting actual monitoring operating datato obtain a length N_(x) of the monitoring time series used for asmoothing calculation.

Processing missing data, for a missing sequence {x_(t), t=1, 2, . . . }formed by original monitoring data, firstly obtaining the length N_(x)of the monitoring time series of the original data required for thesmoothing calculation according to an actual monitoring operating dataanalysis; and setting a count of smoothing steps m, and for gasconcentration monitoring values {x_(t), t=1, 2, . . . , N_(x)} of thefirst N_(x) points of missing data points, continuously performing thesmoothing calculation of m steps to obtain a final smoothed valueF_(t+m), and finally inserting the final smoothed value F_(t+m) into themissing sequence to form a complete monitoring data time series.

In the embodiment, considering a possibility of error propagation, adynamically correcting prediction method is adopted, that is, reservingARMA model parameters when the prediction error is the smallest, ormodel parameters when the prediction error is relatively small byevaluating a prediction error after each prediction, and establishing amodel with the two model parameters, and using the current data toestablish a model at the same time, and by taking an average value ofseveral prediction results, the prediction result can be corrected,which can not only make a prediction along a direction of errorreduction, but also make the prediction result be closer to a gasconcentration at an average trend of change in a future period,improving a closeness degree of the change trend of gas concentration.Therefore, a dynamically correcting ARMA prediction method includes thefollowing steps.

Evaluating a predicted error, for previous j−1 predictions, calculatingan average value of prediction errors of previous n predictions, andobtaining an error minimum value and an error subminimum value.

Determining an effective model order, determining model orders p₁ and p₂when the predicted error minimum value and the error subminimum valueare obtained as effective model orders of the previous j−1 predictions.

Modeling with current data, for an analysis sequence formed by operatingmonitoring data of the current LNG storage device, obtaining an optimalorder p₀ through the ARMA model for parameter estimation and validityinspection.

Predicting model, taking p=p₀, p₁, p₂ as an order respectively toperform an operating data parameter prediction, and obtaining predictionresults X=[x_(j1), x_(j2), x_(j3)].

Calculating the best prediction result, calculating an average value ofeach element of X=[x_(j1), x_(j2), x_(j3)] to obtain a final predictionresult as the best prediction result.

In the embodiment, step 4 specifically includes: the field maintenancepersonnel sending processing information to the LNG distributed energymanagement platform through the sensor network after completing theinspection and processing, and the LNG distributed energy managementplatform confirming whether the processing is completed; the LNGdistributed energy management platform obtaining processed tankperception information through the LNG distributed energy storage sensornetwork platform and confirming that the field maintenance personnelcompletes the processing if a status of the processed tank perceptioninformation is normal, and feeding back to the field maintenancepersonnel.

Embodiment 2

In the embodiment, an Internet of Things system for monitoringdistributed energy storage safety is provided, which is implemented byusing a method for monitoring distributed energy storage safety inembodiment one. FIG. 3 is a schematic diagram illustrating a method formonitoring distributed energy storage safety according to someembodiments of the present disclosure. As shown in FIG. 3 , the systemincludes an LNG distributed energy object platform, a sensor networkplatform, an LNG distributed energy management platform, a serviceplatform, and a user platform.

The LNG distributed energy object platform may be a functional platformfor generating perception information and executing control information.The LNG distributed energy object platform may include an LNGdistributed energy storage object platform (not shown in FIG. 3 ) and anLNG distributed energy storage maintenance personnel object platform(not shown in FIG. 3 ). The LNG distributed energy storage objectplatform is configured to monitor and perceive operating data of an LNGstorage device, and transmit the perception information after symmetricencryption to the LNG distributed energy management platform through thesensor network platform; the LNG distributed energy storage maintenancepersonnel object platform is configured for field maintenance personnelto receive an alarm alert and feedback on maintenance processing.

The sensor network platform may be a functional platform for managingsensor communication. The sensor network platform may include an LNGdistributed energy storage sensor network platform (not shown in FIG. 3) and an LNG distributed energy storage maintenance personnel sensornetwork platform (not shown in FIG. 3 ), which are configured to realizea communication connection for perception and control between the LNGdistributed energy management platform and the LNG distributed energyobject platform.

The LNG distributed energy management platform may be a platform thatprovides perception management and control management functions for anoperating system of the Internet of Things system. The managementplatform may perform an anomaly judgment and anomaly prediction analysisbased on acquired operating data and send the alarm alert to fieldmaintenance personnel for inspection and processing according to a tanknumber of a storage device corresponding to abnormal data obtained bythe anomaly judgment and anomaly prediction analysis. In someembodiments, the LNG distributed energy management platform may furtherbe configured to obtain the operating data of the LNG storage device,physical and chemical parameters of LNG in the LNG storage device, anddetermine at least one set of pressure change data at at least onefuture time point of the LNG storage device, and then determine apressure adjusting time point, and prepare for a pressure adjustment.

The service platform may be a platform for receiving and transmittingdata and/or information. The service platform is configured to obtainperception information demanded by a user from the LNG distributedenergy management platform for analysis and storage, and receive controlinformation sent by the user for processing and send processed controlinformation to the LNG distributed energy management platform.

The user platform may be a platform for interacting with a user. Theuser platform may be configured as a terminal device, or the like. Theuser platform may obtain the operating data of the LNG storage devicefrom the service platform for various users, and send controlinformation to the service platform.

Through closed-loop management formed by a functional structure of theInternet of Things system with five platforms, informatization andintelligence are realized. Through a detailed and clear division oflabor of platforms, the abnormal information is monitored in real time,which improves the efficiency of problem handling and makes theinformation processing of the Internet of Things system smoother andmore efficient.

In some embodiments, the Internet of Things system for monitoringdistributed energy storage safety in the present disclosure may alsoperform safety management on LNG storage.

FIG. 4 is a flowchart illustrating an exemplary process for managing LNGstorage safety according to some embodiments of the present disclosure.In some embodiments, a process 400 may be executed by an LNG distributedenergy management platform. As shown in FIG. 4 , a process 400 includesthe following steps.

Step 410, acquiring operating data of an LNG storage device.

The LNG storage device refers to a device for storing LNG, or the like.For example, an LNG storage tank, etc. For more information about theLNG storage device, please refer to the relevant descriptions of the LNGstorage device in FIG. 1 .

The operating data refers to relevant parameters of operation of the LNGstorage device, etc. In some embodiments, the operating data may atleast include thermal conductivity, environment temperature, and storagetemperature. For more information about the operating data, please referto the relevant descriptions of the operating data in FIG. 1 .

The LNG distributed energy management platform may obtain the operatingdata of the LNG storage device in various ways. For example, the LNGdistributed energy management platform may acquire the operating data ofthe LNG storage device through a data acquiring unit. For moreinformation about the data acquiring unit, please refer to FIG. 1 andits related descriptions.

In some embodiments, the LNG distributed energy management platform mayrespond to a change in the quality of stored LNG to determine a changedstorage temperature based on the operating data and physical andchemical parameters. For example, the distributed energy managementplatform may set weights of each operating data and physical andchemical parameter, and determine the changed storage temperature byweighted calculation. A specific weight may be set according toexperience.

Step 420, acquiring physical and chemical parameters of LNG in the LNGstorage device.

The physical and chemical parameters refer to parameters related tophysical properties and chemical properties of substances. In someembodiments, the physical and chemical parameters may at least include atype, pressure, and quality of the LNG at a plurality of consecutivetime points.

The LNG distributed energy management platform may obtain the physicaland chemical parameters of the LNG in the LNG storage device in variousways. For example, the LNG distributed energy management platform mayacquire the physical and chemical parameters of the LNG in the LNGstorage device through the data acquiring unit.

Step 430, determining at least one set of pressure change data at atleast one future time point of the LNG storage device based on theoperating data and physical and chemical parameters.

The pressure change data refers to parameters related to a pressurechange of the LNG in the LNG storage device.

The LNG distributed energy management platform may determine the atleast one future time point in various ways. For example, the LNGdistributed energy management platform may set a first preset rulerelated to selection of the future time point based on the operatingdata and physical and chemical parameters and determine the at least onefuture time point according to the first preset rule, and the firstpreset rule may set according to experience. For example, the firstpreset rule may be that, when the operating data is r and the physicaland chemical parameters are t, a time point after u min from a currenttime point is selected as the future time point.

In some embodiments, the LNG distributed energy management platform mayalso determine the at least one future time point according to pseudodata information. For more details, please refer to FIG. 6 and itsrelated descriptions.

Understandably, pressure in the LNG storage device is constantlychanging. The thermal conductivity, environment temperature, etc. in theoperating data, and the type and quality of the LNG in the physical andchemical parameters can affect evaporation of the LNG in the LNG storagedevice, thereby affecting the pressure in the LNG storage device.Therefore, the LNG distributed energy management platform may determineat least one set of pressure change data at least one future time pointin the LNG storage device based on the operating data and physical andchemical parameters.

The LNG distributed energy management platform may determine at leastone set of pressure change data at the at least one future time point ofthe LNG storage device in a plurality of ways based on the operatingdata and physical and chemical parameters. For example, the LNGdistributed energy management platform may organize historical data suchas historical operating data, historical physical and chemicalparameters, and historical pressure change data at historical timepoints into a data comparison table, and determine the at least one setof pressure change data at the at least one future time point based onthe data comparison table.

In some embodiments, the LNG distributed energy management platform mayfurther determine the at least one set of pressure change data at the atleast one future time point based on the operating data, physical andchemical parameters, historical pressure change data, and pseudo datainformation. For more details, please refer to FIG. 5 and its relateddescriptions.

step 440, determining a pressure adjusting time point and preparing fora pressure adjustment based on the at least one set of pressure changedata at the at least one future time point.

The pressure adjusting time point refers to a time point when a pressureadjusting operation is performed.

Pressure adjustment preparation refers to preparation work before thepressure adjusting operation is performed, for example, checking asealing condition.

The LNG distributed energy management platform may determine thepressure adjusting time point and prepare for the pressure adjustment invarious ways based on the at least one set of pressure change set at theat least one future point. For example, the LNG distributed energymanagement platform may set a preset pressure threshold, compare the atleast one set of pressure change data at the at least one future timepoint with the preset pressure threshold, and determine a correspondingfuture time point when pressure change data exceeds the preset pressurethreshold as the pressure adjusting time point, and prepare for thepressure adjustment.

In some embodiments, the LNG distributed energy management platform maydetermine a candidate time point when the at least one set of pressurechange data reaches the preset pressure threshold based on the at leastone set of pressure change data at the at least one future time point;and the pressure adjusting time point is determined based on thecandidate time point and a pseudo data feature. For more details aboutthe pseudo data feature, please refer to FIG. 6 and its relateddescriptions. For more information about the pseudo data, please referto FIG. 1 , FIG. 5 , and their related descriptions.

The candidate time point refers to a corresponding future time pointwhen the pressure change data meets a certain condition.

In some embodiments, the LNG distributed energy management platform maycompare the at least one set of pressure change data at the at least onefuture time point with the preset pressure threshold, and determine acorresponding future time point when the pressure change data exceedsthe preset pressure threshold as the candidate time point.

In some embodiments, the LNG distributed energy management platform maydetermine the pressure adjusting time point in various ways based on thecandidate time point and pseudo data feature. For example, the LNGdistributed energy management platform may construct a target vectorbased on the candidate time point and pseudo data feature, and determinethe pressure adjusting time point through a vector database.

The vector database refers to a database for storing, indexing, andquerying vectors. Through the vector database, a similarity query andother vector management may be quickly performed on a large number ofvectors.

The vector database may include a plurality of reference vectors andreference adjusting time points corresponding to the plurality ofreference vectors. The reference vector may be constructed based onhistorical candidate time points and a historical pseudo data feature.The reference pressure adjusting time point corresponding to thereference vector may be obtained according to an actual pressureadjusting time point corresponding to the historical data.

The LNG distributed energy management platform may determine a referencevector that meets a preset condition as an associated vector bysearching the vector database based on the target vector, and use areference pressure adjusting time point corresponding to the associatedvector as the pressure adjusting time point. The preset condition may bethat a vector distance is smaller than a distance threshold, the vectordistance is the smallest, or the like. The distance threshold may be asystem default value, an experience value, an artificial preset value,or any combination thereof, and may be set according to an actual need,which is not limited in the present disclosure.

In some embodiments, the LNG distributed energy management platform mayadjust the candidate time point based on the pseudo data feature todetermine the pressure adjusting time point. For example, the LNGdistributed energy management platform may calculate the pressureadjusting time point through equation (1) based on pseudo data featureand the candidate time point.

t=t ₀ ×e ^((−w))  (1)

Where t denotes the pressure adjusting time point, to denotes thecandidate time point, and W denotes pseudo data feature coefficientsgreater than or equal to 0, which may be obtained according to thepseudo data feature. For example, the greater the count of pseudo dataand the more densely it distributed, the larger the W.

It is understandable that the more serious the pseudo data is (thegreater the count of pseudo data and the denser its distribution), themore appropriate the pressure adjusting time point should be advanced toensure the timeliness of the pressure adjusting operation. According tosome embodiments of the present disclosure, based on the pseudo datafeature, determining the pressure adjusting time point by adjusting thecandidate time point can determine a pressure adjusting time point inline with reality according to severity of the pseudo data, ensuring thetimeliness of a pressure adjustment control and leaving sufficientpreparation time for field maintenance personnel.

According to some embodiments of the present disclosure, determining thecandidate time point when the preset pressure threshold is reached, andcombining with the pseudo data feature to determine the pressureadjusting time point can consider an impact of the pseudo data featureon the pressure adjusting time point, making a determination processmore precise.

In some embodiments, the LNG distributed energy management platform mayobtain actual pressure change data; perform a failure analysis on theLNG storage device based on the actual pressure change data and the atleast one set of pressure change data at the at least one future timepoint.

The actual pressure change data refers to an actual value of thepressure change data corresponding to the at least one future timepoint.

The LNG distributed energy management platform may obtain the actualpressure change data in various ways. For example, the LNG distributedenergy management platform may obtain the actual pressure change datathrough the data acquiring unit.

It is understandable that when a situation where a difference betweenthe actual pressure change data and the least one set of pressure changedata at the at least one future time point exceeds a certain rangeoccurs frequently, there may be a failure in the LNG storage device, anda failure analysis is required. The failure analysis may include judginga failure type, etc.

In some embodiments, the LNG distributed energy management platform mayperform a data comparison, if a count of pressure change data whosedifference with the actual pressure change data exceeds a presetdifference threshold exceeds a preset count threshold, it is determinedthat there is a failure in the LNG storage device.

In some embodiments, the LNG distributed energy management platform mayobtain a difference distribution time point based on the actual pressurechange data and the at least one set of pressure change data at the atleast one future time point; Based on the pseudo data feature, obtain adistribution time point of the pseudo data; determine a similarity basedon the difference distribution time point and the distribution timepoint of the pseudo data; and determine the failure type based on theactual pressure change data, the at least one set of pressure changedata at the at least one future time point, and the similarity.

The LNG distributed energy management platform may perform the datacomparison, and determine a future time point corresponding to pressurechange data whose difference with the actual pressure change dataexceeds the preset difference threshold as the difference distributiontime point.

The LNG distributed energy management platform may determine a timepoint when there is pseudo data as a pseudo data distribution time pointbased on the pseudo data feature. For more information about the pseudodata feature, please refer to FIG. 6 and its related descriptions.

The similarity refers to a parameter representing a degree of similaritybetween the difference distribution time point and the pseudo datadistribution time point.

In some embodiments, the LNG distributed energy management platform maydetermine the similarity in various ways based on the differencedistribution time point and the pseudo data distribution time point. Forexample, the LNG distributed energy management platform may compare thedifference distribution time point with the pseudo data distributiontime point, determine a time point when the difference between thedifference distribution time point with the pseudo data distributiontime point is less than a preset distribution time threshold as asimilar distribution time point, and determine a ratio of count ofsimilar distribution time points to the difference distribution timepoint as the similarity.

The failure type may include an LNG storage device failure, a dataacquiring unit failure, an external interference, etc.

In some embodiments, the LNG distributed energy management platform maydetermine the failure type in various ways based on the actual pressurechange data, the at least one set of pressure change data at the atleast one future time point and the similarity. For example, the LNGdistributed energy management platform may determine the failure typeaccording to a second preset rule. The second preset rule may includedetermining the failure type as an external interference type if thesimilarity between the difference distribution time point and the pseudodata distribution time point exceeds a similarity threshold; The secondpreset rule may also include that if the difference between the actualpressure change data and the at least one set of pressure change data atthe at least one future time point exceeds a preset differencethreshold, and a count of the pressure change data whose the differenceexceeds the preset difference threshold exceeds a preset differencecount threshold, and the similarity between the difference distributiontime point and the pseudo data distribution time point does not exceed asimilarity threshold, then it is determined the failure type as afailure type of an LNG storage device.

For more information about the failure analysis, please refer to therelated descriptions of the anomaly judgment and anomaly predictionanalysis performed on the acquired operating data in FIG. 2 .

It is understandable that due to the interference of the externalenvironment, there may be varying amounts of pseudo data in the pressurechange data, which may have a certain impact on pressure prediction andfailure analysis. By judging the failure type based on the actualpressure change data, at least one set of pressure change data andsimilarity of at least one future time point described in someembodiments of this disclosure, it can reasonably and reliably determinewhether the data difference is caused by external interference, thus,the accuracy of failure analysis can be improved.

According to some embodiments of the present disclosure, performing thefailure analysis on the LNG storage device based on the actual pressurechange data and the at least one set of pressure change data at the atleast one future time point can comprehensively consider various factorsthat affect a failure analysis result, making a determination process ofthe failure analysis accurate and reasonable and helping staff to dealwith a related failure in time.

In some embodiments of the present disclosure, through acquiringoperating data and physical and chemical parameters and determining theat least one set of pressure change data at the at least one future timepoint to determine the pressure adjusting time point and prepare for thepressure adjustment can monitor and analyze a pressure situation in theLNG storage device intelligently, accurately predict a future pressurechange and reduce a cost and error of manual monitoring at the sametime, facilitate to detect an abnormal situation in advance and arrangestaff arranged to deal with the abnormal situation in time to avoidpotential safety problems.

FIG. 5 is a flowchart illustrating an exemplary process for determiningat least one set of pressure change data at at least one future timepoint according to some embodiments of the present disclosure. In someembodiments, a process 500 may be executed by an LNG distributed energymanagement platform. As shown in FIG. 5 , the process 500 includes thefollowing steps.

Step 510, acquiring historical pressure change data in an LNG storagedevice.

The historical pressure change data refers to pressure change data in ahistorical time.

The LNG distributed energy management platform may obtain the historicalpressure change data in various ways. For example, the LNG distributedenergy management platform may obtain the historical pressure changedata through a storage device.

Step 520, determining pseudo data information based on the historicalpressure change data.

The pseudo data information may be information representing whetherpseudo data exists or not, and a size of the pseudo data. The pseudodata may be data with an error due to external environment interferenceand other factors. For more information about the pseudo data, pleaserefer to FIG. 1 and its related descriptions. The pseudo datainformation may reflect a degree of an impact of an interference factoron pressure monitoring.

The LNG distributed energy management platform may determine the pseudodata information in various ways based on the historical pressure changedata. For example, the LNG distributed energy management platform mayscreen the historical pressure change data through an anomaly judgmentcondition, and then perform a pseudo data verification on screenedabnormal information through a pseudo data verification manner todetermine the pseudo data information. For more information about thepseudo data verification manner and anomaly judgment condition, pleaserefer to the relevant descriptions in FIG. 1 and FIG. 2 .

Step 530: determining the at least one set of pressure change data atthe at least one future time point based on operating data, physical andchemical parameters, the historical pressure change data, and the pseudodata information.

In some embodiments, the LNG distributed energy management platform maydetermine the at least one set of pressure change data at the at leastone future time point in various ways based on the operating data, thephysical and chemical parameters, the historical pressure change data,and the pseudo data information. For example, the LNG distributed energymanagement platform may construct a target vector based on the operatingdata, the physical and chemical parameters, the historical pressurechange data, and the pseudo data information, and determine the at leastone set of pressure change data at the at least one future time pointthrough a vector database. For a specific method of determinationthrough the vector database, please refer to the method of determining apressure adjusting time point through the vector database in FIG. 4 .

In some embodiments, the LNG distributed energy management platform maydetermine a count and step size of the at least one future time pointaccording to the pseudo data information. For details, please refer toFIG. 6 and its related descriptions.

In some embodiments, the LNG distributed energy management platform maypredict the at least one set of pressure change data at the at least onefuture time point through a pressure model based on the operating data,the physical and chemical parameters, the historical pressure changedata, and the pseudo data information.

The pressure model may be a model that determines the at least one setof pressure change data at the at least one future time point. In someembodiments, the pressure model may be a machine learning model with acustomized structure hereinafter. The pressure model may also be amachine learning model with other structures, such as a neural network(NN) model.

In some embodiments, the pressure model may include a feature extractinglayer and a pressure layer.

The feature extracting layer may be configured to extract a pressurechange feature of the pressure change data. An input of the featureextracting layer may include the step size (the forward predicted stepsize np), the historical pressure change data, the pseudo datainformation, and an output of the feature extracting layer may includethe pressure change feature. The pressure change feature refers tofeature data required to determine at least one set of future pressurechange data at the at least one future time point. For example, pressurechange data without pseudo data, data of a preset time step size. Formore information about the step size, please refer to the relateddescriptions in FIG. 2 and FIG. 4 . The feature extracting layer may bea long short-term memory (LSTM) model.

The pressure layer may be configured to determine the at least one setof future pressure change data at the at least one future time point. Aninput of the pressure layer may include the pressure change feature, theoperating data, and the physical and chemical parameters, and an outputof the pressure layer may include the at least one set of futurepressure change data at the at least one future time point. For moreinformation about the operating data, the physical and chemicalparameters, and the at least one set of future pressure change data atthe at least one future time point, please refer to FIG. 4 and itsrelated descriptions. The pressure layer may be a recurrent neuralnetwork (RNN) model, or the like.

In some embodiments, an output range of the pressure model may beaffected by the step size.

It is understandable that the larger the count of the pseudo data, theless sample data available for prediction, and the step size may becorrespondingly reduced to ensure accuracy of the prediction. Thesmaller the step size, the smaller the count of time points availablefor the prediction, and the output range of the pressure model decreasesaccordingly.

By determining the step size through the pseudo data informationdescribed in some embodiments of the present disclosure, the accuracy ofthe output of the pressure model can be improved to a certain extent,meeting a requirement for use and data processing.

In some embodiments, the feature extracting layer and the pressure layermay be obtained through joint training. In some embodiments, a firsttraining sample of the joint training includes a sample step size,sample historical pressure change data, sample pseudo data information,sample operating data, sample physical and chemical parameters, and afirst label is an actual value of the at least one set of futurepressure change data at the at least one future time point. Inputtingthe sample step size, the sample historical pressure change data, andthe sample pseudo data information into the feature extracting layer,the pressure change feature output by the feature extracting layer isobtained. Using the pressure change feature as the first training sampledata, and inputting the first training sample data together with thesample operating data and the sample physical and chemical parametersinto the pressure layer, then the at least one set of future pressurechange data at the at least one future time point is obtained. Based onthe actual value of at least one set of future pressure change data atthe at least one future time point and at least one set of futurepressure change data at the at least one future time point output by thepressure layer, a loss function is constructed and parameters of thefeature extracting layer and the pressure layer are synchronouslyupdated. Through parameter update, a trained feature extracting layerand a trained pressure layer are obtained.

By predicting at least one set of pressure change data through thepressure model described in some embodiments of the present disclosure,multiple factors affecting the pressure change data and a relationshipbetween the multiple factors can be comprehensively considered to obtainaccurate pressure change data.

In some embodiments of the present disclosure, determining the at leastone set of pressure change data at the at least one future time pointbased on the operating data, the physical and chemical parameters, thehistorical pressure change data, and the pseudo data information canconsider an impact of the pseudo data information on a process ofdetermining the pressure change data, improving accuracy of a pressurechange data prediction and reducing an error caused by an interferencefactor.

FIG. 6 is a schematic diagram illustrating an exemplary process fordetermining at least one future time point according to some embodimentsof the present disclosure.

In some embodiments, an LNG distributed energy management platform maydetermine a pseudo data feature 620 based on pseudo data information610. Based on the pseudo data feature 620, a count of the at least onefuture time point 630 and a step size 680 are determined.

The pseudo data feature 620 refers to feature information related topseudo data. For example, the pseudo data feature 620 may include acount of the pseudo data, a distribution feature of the pseudo data, orthe like.

The distribution feature of the pseudo data may include a concentrationdegree of a pseudo data sequence, an order situation of the pseudo datasequence in a time series, or the like. The concentration degree of thepseudo data sequence refers to a parameter representing a degree ofconcentration of pseudo data sequence distribution. For example, if atime series contains 500 data, wherein 109th to 112th data according toa chronological order are pseudo data, then the 109th to 112th dataforms a pseudo data sequence, and a concentration degree of the pseudodata sequence is 4. The order situation of the pseudo data sequence in atime series refers to an order average of the pseudo data sequence or aratio of an order of a first pseudo data in a pseudo data sequence. Forexample, the order of the pseudo data sequence in the time series may beexpressed as 111 (round to an integer) by the order average, and may beexpressed as 21.8% ( 109/500) by the ratio of an order of a first pseudodata in a pseudo data sequence.

In some embodiments, the LNG distributed energy management platform maydetermine the pseudo data feature 620 in various ways based on thepseudo data information 610. For example, the LNG distributed energymanagement platform may determine the pseudo data feature 620 through adetermination model based on the pseudo data information 610.

The determination model is a model that determines the pseudo datafeature 620. In some embodiments, the determination model may be a NNmodel, an RNN model, or the like.

An input of the determination model may be the pseudo data information610, and an output of the determination model may be the pseudo datafeature 620.

The determination model may be obtained by training a plurality of thirdtraining samples with labels.

The third training sample may include sample pseudo data information,which may be obtained based on historical data. The labels may includeactual pseudo data features, which may be manually labeled.

Step size 680 (the forward predicted step size np) refers to a timeinterval between future time points for sampling. For more informationabout the step size, please refer to the relevant descriptions of theforward prediction step size np in FIG. 2 .

In some embodiments, the LNG distributed energy management platform maydetermine the count of the at least one future time point 630 and thestep size 680 in various ways based on the pseudo data feature 620. Forexample, the LNG distributed energy management platform may determinethe count of the at least one future time point 630 and the step size680 based on the pseudo data feature 620 through a third preset rule.The third preset rule may include a corresponding relationship betweenthe pseudo data feature 620 and the count of the at least one futuretime point 630. For example, the third preset rule may include: thecount of future time points=a*a count of the pseudo data, where a is acoefficient set according to experience.

The third preset rule may also include an adjustment relationshipbetween the pseudo data feature 620 and a step size of the at least onefuture time point. For example, the third preset rule may includesetting a concentration degree threshold as e, an initialization valueof the step size as f, and whenever the concentration degree of thepseudo data sequence in the pseudo data feature 620 is higher than theconcentration degree threshold e by a value h, the LNG distributionenergy management platform may reduce g as the step size from theinitialization value of the step size f. The concentration degreethreshold refers to a preset concentration degree threshold of thepseudo data sequence. The third preset rule may be set based onexperience, a user requirement, or the like.

It is understandable that when the count of pseudo data is relativelylarge, sample data available for prediction may reduce. At this time,reducing the step size 680 can make up for an accuracy loss caused by alarge count of the pseudo data to a certain extent; however, if thecount of pseudo data is relatively small, it is not necessary to set thestep size 680 to be too small, so as to prevent efficiency of predictionfrom being affected by a too-small step size 680.

When the concentration degree of the pseudo data sequence is relativelyhigh, it leads to an uneven distribution of real data in the time series(e.g., data missing at several consecutive time points). At this time,reducing the step size 680 may make up for the accuracy loss caused by ahigh concentration degree of the pseudo data sequence to a certainextent.

In some embodiments, the LNG distributed energy management platform maydetermine an initialization value of a step size 640 based on the pseudodata feature 620. The initialization value of a step size 640 is theinitialization value np₀ of the anomaly prediction analysis. For moreinformation about the initialization value of a step size, please referto the relevant descriptions of the initialization value of the anomalyprediction analysis in FIG. 2 .

In some embodiments, the LNG distributed energy management platform maydetermine the initialization value of a step size 640 in various waysbased on the pseudo data feature 620. For example, the LNG distributedenergy management platform may determine the initialization value of astep size 640 through a data comparison table based on the pseudo datafeature 620. As an example only, the LNG distributed energy managementplatform may organize a plurality of historical pseudo data feature andcorresponding historical initialization values of a step sizecorresponding to the plurality of historical pseudo data feature intothe data comparison table, and determine the initialization value of astep size 640 based on the data comparison table.

In some embodiments, the LNG distributed energy management platform maycalculate the step size 680 based on the initialization value of a stepsize 640 through equation (2).

$\begin{matrix}{{np} = {{np}_{0} - {\lambda{\sum_{i = 1}^{n}\frac{\left( {k_{i} - w_{i}} \right)}{n}}}}} & (2)\end{matrix}$

Where np denotes the step size 680, np₀ denotes the initialization valueof a step size 640, λ denotes a coefficient greater than 0 obtainedbased on experience, k_(i) denotes a concentration degree of an i-thpseudo data sequence, and w_(i) denotes a weight of the concentrationdegree of the i-th pseudo data sequence, the weight is related to theorder situation of the pseudo data sequence in the time series, thefurther back the order, the greater the weight; n denotes a total countof data in the time series; and 1≤i≤n.

It is understandable that the earlier the order of the pseudo datasequence in the time series, that is, the earlier an occurrence time,meaning that the data is older and has a relatively small impact onprediction accuracy; the later the order of the pseudo data in the timeseries, that is, the later the occurrence time, meaning that the data isnewer and has a relatively greater impact on the prediction accuracy.

In some embodiments, the LNG distributed energy management platform mayadjust the initialization value of a step size 640 through an adjustingmodel 650 based on the pseudo data feature 620 to determine a dynamicstep size 680.

The adjusting model 650 is a model that determines the dynamic step size680. In some embodiments, the adjusting model 650 may be a neuralnetwork (NN) model, or the like.

An input of the adjusting model 650 may be a pre-update step size of theat least one future time point 660 and a pseudo data sequence featurevector. An output of the adjusting model 650 may be a post-update stepsize of the at least one future time point 670. The pre-update step sizeof at least one future time point 660 refers to a step size that needsto be updated during a dynamic updating process of the step size.

In some embodiments, the LNG distributed energy management platform maydetermine the pseudo data sequence feature vector based on the pseudodata feature 620, for example, the LNG distributed energy managementplatform may determine the pseudo data sequence feature vector as [(j₁,k₁), (j₂, k₂), (j₃, k₃), (j₄, k₄)], where j₁-j₄ are the concentrationdegrees of the pseudo data sequence 1-4, and k₁-k₄ are the orders of thepseudo data sequence 1-4 in the time series.

The adjusting model 650 may be obtained by training a plurality ofsecond training samples with labels. A plurality of second trainingsamples with labels may be input into an initial adjusting model, a lossfunction is constructed through the labels and outputs of the initialadjusting model, and parameters of the initial adjusting model areiteratively updated based on the loss function. When the loss functionof the initial adjusting model satisfies a set condition, a modeltraining is completed, and a trained adjusting model 650 is obtained.The set condition may include one or more conditions such as the lossfunction being smaller than a threshold, converging, or a trainingperiod of the loss function reaching a threshold.

The second training samples may include a pre-step size of a samplefuture time point and a sample pseudo data sequence feature vector,which may be obtained based on historical data. The labels may includean actual post-update step size. In some embodiments, the LNGdistributed energy management platform may determine an actual step sizecorresponding to a prediction result whose prediction accuracy is higherthan an accuracy threshold in the historical data corresponding to thesample data as a label.

It is understandable that the pre-update step size of the at least onefuture time point 660 inputting to the adjusting model 650 may be theinitialization value of a step size 640, and combining with a pseudodata sequence feature vector of a current time point 1, the post-updatestep size of the at least one future time point 670 may be outputted.After a certain period, the post-updated step size of the at least onefuture time point may continue to be used as the pre-update step size ofthe at least one subsequent future time point inputted to the adjustingmodel 650, combining with a pseudo data sequence feature vector of acurrent time point 2, a post-update step size of the at least one moresubsequent future time point may be outputted, so as to realize adynamic adjustment of the step size 680.

According to some embodiments of the present disclosure, determining thedynamic step size by adjusting the initialization value through theadjusting model based on the pseudo data feature can continuously adjusta step size intelligently based on an actual situation of data, so as toobtain a dynamic step size more in line with reality.

In some embodiments, during the dynamic adjusting process, the step size680 may not exceed a preset lower limit.

In some embodiments, the LNG distributed energy management platform maydetermine the preset lower limit based on an anomaly processing responsetime. For example, the LNG distributed energy management platform maydetermine the anomaly processing response time as the preset lowerlimit.

The anomaly processing response time refers to a time required for staffto process after abnormal data is predicted.

It is understandable that although reducing the step size can make theprediction more accurate, the step size is too small, the abnormal datais predicted, the staff may not have enough time to process it. In someembodiments of the present disclosure, setting the step size notexceeding the preset lower limit during the dynamic adjusting processcan avoid a situation that the staff may have no time to deal with anabnormal situation as much as possible.

According to some embodiments of the present disclosure, determining thepseudo data feature and then further determining the count and the stepsize of the at least one future time point based on the pseudo datainformation can comprehensively consider various factors that affectaccuracy of a prediction result, and determine a more accurate count anda step size of future time points.

One or more embodiments of the present disclosure also provide anon-transitory computer-readable storage medium storing computerinstructions, and when a computer reads the computer instructions storedin the storage medium, the computer executes an LNG storage safetymanagement method in any one of the above-mentioned embodiments.

The basic principles, main features, and advantages of the presentdisclosure have been shown and described above. Those skilled in theindustry should understand that the present disclosure is not limited bythe above-mentioned embodiments. What is described in theabove-mentioned embodiments and the description only illustrates theprinciple of the present disclosure. Without departing from the spiritand scope of the present disclosure, the present disclosure may alsohave possible variations and improvements, which all fall within thescope of the claims of the present invention. The scope of the claims ofthe present disclosure is defined by the appended claims and theirequivalents.

What is claimed is:
 1. A method for monitoring distributed energystorage safety, comprising: step 1: monitoring, by utilizing a dataacquiring unit, a liquefied natural gas (LNG) storage device, perceivingand acquiring pressure, temperature, and position data on the LNGstorage device, obtaining encrypted perception information throughperforming an analog-to-digital conversion on perception information bythe data acquiring unit and symmetrically encrypting the perceptioninformation by adopting a microsoft point-to-point encryption (MPPE) andInternet Protocol Security (IPSec) mechanism in a binary mode, andmanaging key by a public-private key verification; actively sending, bythe data acquiring unit, authentication information to an LNGdistributed energy management platform at a designated address throughan LNG distributed energy storage sensor network platform, after passinga two-way symmetric authentication, establishing a unique communicationchannel between the data-acquiring unit and the LNG distributed energymanagement platform to transmit the encrypted perception information;step 2: decrypting, by the LNG distributed energy management platform,the encrypted perception information, performing an anomaly judgment ondecrypted perception information according to a preset anomaly judgmentcondition, and screening out abnormal perception information; performinga pseudo data verification on the abnormal perception informationutilizing a pseudo data verification manner, identifying and labeling atype of pseudo data caused by an external environmental interference;performing an anomaly prediction analysis on operating data of the LNGstorage device according to an early warning mechanism; wherein theperforming an anomaly prediction analysis on operating data of the LNGstorage device according to an early warning mechanism includes: datapreprocessing: adopting a Holt double-parameter linear exponentialsmoothing method to smooth the decrypted perception information toobtain a monitoring time series x_(t); model initialization: aninitialization model order p=1, a forward predicted step size np=np₀;model establishment: establishing an initial auto-regression movingaverage (ARMA) model based on the monitoring time series x_(t);determining a length of a modeling sample: determining an integermultiple of an inverse of an interval between two adjacent frequenciesin a temporal frequency domain of the perception information as thelength of the modeling sample through time series analysis; estimatingmodel parameter: estimating the model parameter by utilizing a leastsquare method; inspecting the model and determining an order:determining a machine order p of a parameter change trend predictingmodel to obtain a final parameter trend predicting model ARMA (2p, 2p−1)by adopting an Akaike information criterion (AIC); predicting parameter:obtaining a prediction interval by calculating a continuous forwardpredicted step size np; and analyzing the abnormal data: obtaining anoperating prediction result of the LNG storage device throughcalculating a best prediction result and a corresponding predictioninterval corresponding to the best prediction result by adopting adynamically correcting ARMA prediction method, and determining whetherthe operating prediction result is the abnormal data according to thepreset anomaly judgment condition; step 3: sending, by an LNGdistributed energy storage maintenance personnel sensor networkplatform, an alarm prompt to field maintenance personnel for aninspection and processing according to a tank number of a storage devicecorresponding to the abnormal data obtained by the anomaly predictionanalysis and anomaly judgment; and step 4: sending, by the fieldmaintenance personnel, processing information to the management platformthrough the LNG distributed energy storage maintenance personnel sensornetwork after completing the inspection and processing, and confirming,by the management platform, whether the processing is completed;obtaining, by the LNG distributed energy management platform, processedtank perception information through the LNG distributed energy storagesensor network platform and confirming that the field maintenancepersonnel completes the processing if a status of the processed tankperception information is normal, and feeding back to the fieldmaintenance personnel.
 2. The method according to claim 1, wherein theperforming a pseudo data verification on the abnormal perceptioninformation by utilizing a pseudo data verification manner includes:establishing the pseudo data verification manner, setting an error codein a sensor program of the data acquiring unit to simulate a sensorvalue during a real electromagnetic interference for pseudo datagenerated by an electromagnetic interference in a field maintenanceprocess in advance, and setting an anomaly analysis result of the LNGdistributed energy management platform as pseudo data of theelectromagnetic interference; for pseudo data generated by atransmission or device failure, randomly creating a sensor ortransmission line failure, and setting the anomaly analysis resultlabeled by the LNG distributed energy management platform as pseudo dataof the sensor or transmission line; and performing a pseudo dataanalysis on the screened abnormal perception information, and labeling acorresponding type of pseudo data by utilizing the pseudo dataverification manner.
 3. The method according to claim 1, wherein thedata preprocessing includes: processing the abnormal perception data,forming the monitoring time series {x_(t), t=1, 2, . . . , N} for theperceived and acquired operating data of the LNG storage device, and forabnormal monitoring data being zero or with a low probability sensorvalue, calculating a one-step smoothing value F_(t) of first N_(x)numbers by monitoring the first N_(x) numbers in the monitoring timeseries to replace the abnormal monitoring data, and selecting actualmonitoring operating data to obtain a length N_(x) of the monitoringtime series used for a smoothing calculation; processing missing data,for a missing sequence {x_(t), t=1, 2 . . . } formed by originalmonitoring data, firstly obtaining the length N_(x) of the monitoringtime series of the original data required for the smoothing calculationaccording to an actual monitoring operating data analysis; and setting acount of smoothing steps m, and for gas concentration monitoring values{x_(t), t=1, 2, . . . , N_(x)} of the first N_(x) points of missing datapoints, continuously performing the smoothing calculation of m steps toobtain a final smoothed value F_(t+m), and finally inserting the finalsmoothed value F_(t+m) into the missing sequence to form a completemonitoring data time series.
 4. The method according to claim 1, whereinthe dynamically correcting ARMA prediction method includes: evaluating apredicted error, for previous j−1 predictions, calculating an averagevalue of prediction errors of previous n predictions, and obtaining anerror minimum value and an error subminimum value; determining aneffective model order, determining model orders p₁ and p₂ when thepredicted error minimum value and the error subminimum value areobtained as effective model orders of the previous j−1 predictions;modeling with current data, for an analysis sequence formed by operatingmonitoring data of the current LNG storage device, obtaining an optimalorder p₀ through the ARMA model for parameter estimation and validityinspection; predicting model, taking p=p₀, p₁, p₂ as an orderrespectively to perform an operating data parameter prediction, andobtaining prediction results X=[x_(j1), x_(j2), x_(j3)]; and calculatingthe best prediction result, calculating an average value of each elementof X=[x_(j1), x_(j2), x_(j3)] to obtain a final prediction result as thebest prediction result.
 5. An Internet of Things system for monitoringdistributed energy storage safety, which is realized by using a methodfor monitoring distributed energy storage safety, wherein the systemincludes an object platform, a sensor network platform, a managementplatform, a service platform, and a user platform; the object platformincludes an LNG distributed energy storage object platform and an LNGdistributed energy storage maintenance personnel object platform; theLNG distributed energy storage object platform is configured to monitorand perceive operating data of an LNG storage device, and transmit theperception information after symmetric encryption to the LNG distributedenergy management platform through a corresponding sensor networkplatform; and the LNG distributed energy storage maintenance personnelobject platform is configured for field maintenance personnel to receivean alarm prompt and feedback on maintenance processing; the sensornetwork platform includes an LNG distributed energy storage sensornetwork platform and an LNG distributed energy storage maintenancepersonnel sensor network platform, which are configured to realize acommunication connection for perception and control between themanagement platform and the object platform; the management platform isconfigured to perform an anomaly judgment and anomaly predictionanalysis based on acquired operating data, and send the alarm prompt tofield maintenance personnel for inspection and processing through thesensor network platform according to a tank number of a storage devicecorresponding to abnormal data obtained by the anomaly judgment andanomaly prediction analysis; the service platform is configured toobtain perception information demanded by a user from the managementplatform for analysis and storage, and receive control information sentby the user for processing and send processed control information to themanagement platform; and the user platform is configured to obtain theoperating data of the LNG storage device from the service platform forvarious users and send the control information to the service platform.